Adding a Column with a Fixed Value in Polars: A Simple Guide
Polars, the fast data manipulation library for Python, provides a powerful way to work with dataframes. But sometimes, you need to add a column filled with a constant value, like a version number or a specific identifier. This article will show you how to achieve this in Polars, based on the question posted on Stack Overflow [https://stackoverflow.com/questions/74147944/polars-create-column-with-fixed-value-from-variable].
The Pandas Approach
The Stack Overflow question highlights the familiar approach in Pandas:
import pandas as pd
version = '1.2.3'
df = pd.DataFrame({'col1': [1, 2, 3]})
df['VERSION'] = version
print(df)
This code creates a Pandas DataFrame with a column named 'VERSION', populated with the value stored in the version variable.
The Polars Way: Using with_column
Polars offers a similar approach using the with_column method:
import polars as pl
version = '1.2.3'
df = pl.DataFrame({'col1': [1, 2, 3]})
df = df.with_column(pl.lit(version).alias('VERSION'))
print(df)
Here's what's happening:
pl.lit(version): This creates a literal column containing the value of theversionvariable..alias('VERSION'): This assigns the name 'VERSION' to the new column..with_column(...): This method adds the newly created column to the existing dataframe.
Key Differences Between Pandas and Polars
While both Pandas and Polars achieve the same outcome, there are some important distinctions:
- Immutability: Polars emphasizes immutability. Instead of modifying the original DataFrame in place (as Pandas does), the
with_columnmethod returns a new DataFrame with the added column. This promotes predictable and efficient data manipulation. - Performance: Polars is generally much faster than Pandas, especially when dealing with large datasets. Its efficient columnar storage and optimized operations contribute to its speed advantage.
Practical Example: Adding a Timestamp
Imagine you're working with data logs that need to be timestamped. Here's how you can add a column with the current timestamp using Polars:
import polars as pl
from datetime import datetime
df = pl.DataFrame({'event': ['start', 'stop', 'continue']})
current_timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
df = df.with_column(pl.lit(current_timestamp).alias('timestamp'))
print(df)
This code creates a new column named 'timestamp' filled with the current timestamp, which can be valuable for tracking data events.
Conclusion
Adding a column with a fixed value is a simple yet essential operation in data manipulation. Polars offers an efficient and intuitive approach using the with_column method. By understanding the concepts of immutability and efficient columnar storage, you can leverage Polars' strengths for fast and reliable data processing.
